|Noshir Contractor (Northwestern University)|
Leveraging Computational Social Science to Address Grand Societal Challenges
The increased access to big data about social phenomena in general, and network data in particular, has been a windfall for social scientists. But these exciting opportunities must be accompanied with careful reflection on how big data can motivate new theories and methods. Using examples of his research in the area of networks, Contractor will argue that Computational Social Science serves as the foundation to unleash the intellectual insights locked in big data. More importantly, he will illustrate how these insights offer social scientists in general, and social network scholars in particular, an unprecedented opportunity to engage more actively in monitoring, anticipating and designing interventions to address grand societal challenges.
[Download slides] (15,42 MB)
Noshir Contractor is the Jane S. & William J. White Professor of Behavioral Sciences in the McCormick School of Engineering & Applied Science, the School of Communication and the Kellogg School of Management at Northwestern University, USA. He is the Director of the Science of Networks in Communities (SONIC) Research Group at Northwestern University and a board member of the Web Science Trust. He is investigating factors that lead to the formation, maintenance, and dissolution of dynamically linked social and knowledge networks in a wide variety of contexts.
|Tina Eliassi-Rad (Network Science, Northeastern University)|
The Reasonable Effectiveness of Roles in Complex Networks
Given a network, how can we automatically discover roles (or functions) of the actors? Roles compactly represent structural behaviors of actors and generalize across various networks. Examples of roles include "clique-members," "periphery-nodes," "bridges," etc. Are there good features that we can extract for actors that indicate role-membership? How are roles different from communities and from equivalences (from sociology)? What are the applications in which these discovered roles can be effectively used? In this talk, we address these questions, provide unsupervised and supervised algorithms for role discovery, and discuss why roles are so effective in many applications from transfer learning to re-identification to anomaly detection to mining time-evolving and multi-relational networks.
[Download slides] (13,56 MB)
Tina Eliassi-Rad is an Associate Professor of Computer Science at Northeastern University in Boston, MA. She is also on the faculty of Northeastern's Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research is rooted in data mining and machine learning; and spans theory, algorithms, and applications of massive data from networked representations of physical and social phenomena. Tina's work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, and cyber situational awareness. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2010, she received an Outstanding Mentor Award from the Office of Science at the US Department of Energy.
|Marko Grobelnik (Artificial Intelligence Laboratory, Jožef Stefan Institute)|
Cross-lingual Global Media Monitoring
Global media monitoring assumes dealing with large amounts of heterogeneous textual data across many languages in a near real-time. We will present a pipeline of machine learning, text mining, and semantic extraction components representing global social dynamics as an evolving network of interrelated events extracted from media. We will demonstrate the proposed approach on an operational system "Event Registry" (http://EventRegistry.org) for (a) collecting media information from over 300,000 news and social media sources, (b) performing linguistic and semantic processing in multiple languages, (c) forming cross-lingual events and event sequences, (d) streaming information about events in open data formats, (e) rich visualizations, and (f) complex queries to analyse global social dynamics. Challenges and technical solutions will be discussed.
[Download slides] (6,57 MB)
Marko Grobelnik works in various aspects of AI from 1985. Focused areas of expertise are Machine Learning, Data/Text/Web Mining, Network Analysis, Semantic Technologies, Deep Text Understanding, and Data Visualization. Marko works as a researcher in AI Lab at Jozef Stefan Institute and is the CEO of Quintelligence.com specialized in solving complex AI problems for the commercial world. He collaborates with major European academic institutions and industries such as Bloomberg, British Telecom, European Commission, Microsoft Research, New York Times. Marko is also co-author of several books, co-founder of several start-ups and is/was involved into over 50 EU funded research projects on various fields of AI.
|Krishna Gummadi (Networked Systems Research Group, Max Planck Institute for Software Systems)|
Discrimination in Human vs. Algorithmic Decision Making
Algorithmic (data-driven) decision making is increasingly being used to assist or replace human decision making in a variety of domains ranging from banking (rating user credit) and recruiting (ranking applicants) to judiciary (profiling criminals) and journalism (recommending news-stories). Against this background, in this talk, I will pose and attempt to answer the following high-level questions:
(a) Can algorithmic decision making be discriminatory?
(b) How robustly can we detect discrimination in human or algorithmic decision making?
(c) Can we control algorithmic discrimination? If so, can algorithmic decisions be used to avoid implicit biases in human decisions?
[Download slides] (1,01 MB)
Krishna Gummadi is a tenured faculty member and head of the Networked Systems research group at the Max Planck Institute for Software Systems (MPI-SWS) in Germany. He received his Ph.D. degree in Computer Science and Engineering from the University of Washington, Seattle.
Krishna's research interests are in the measurement, analysis, design, and evaluation of complex Internet-scale systems. His current projects focus on understanding and building social computing systems. Specifically, they tackle the challenges associated with protecting the privacy of users sharing data online, understanding and leveraging word-of-mouth exchanges to spread information virally, and finding relevant and trustworthy sources of information in online crowds.
Krishna's work on social computing systems, Internet access networks, and peer-to-peer systems has led to a number of widely cited papers, including award (best) papers at ACM COSN, ACM/Usenix's SOUPS, AAAI's ICWSM, Usenix's OSDI, ACM's SIGCOMM IMC, and SPIE's MMCN conferences. He has also co-chaired AAAI's ICWSM 2016, IW3C2 WWW 2015, ACM COSN 2014, and ACM IMC 2013 conferences.
|Petter Holme (Department of Energy Science, Sungkyunkwan University)|
Temporal Networks of Human Interaction
Networks are all around us—from power-grids to the nervous system, from polymer interactions to friendship networks, from protein interactions to chains of historical events. Network theory is a framework that seeks to explain how such networks function, evolve and can be controlled. Like (or, perhaps, as a branch of) statistical physics, it is way to explain how system-wide properties emerges from the microscopic interactions between nodes in the networks. Moreover, network theory gives methods to extract useful information from large-scale data sets of complex systems, thus forming a connection between physics and data science. Sometimes, one has information not only about which nodes that interact, but also when the interaction happens. This information can be crucial for understanding how dynamical systems (like diseases spreading over human contact networks) behave. I will discuss the theory of temporal networks—integrating information about time and network topology. This theory, it turns out, becomes rather different from static network theory (partly because temporal networks are not transitive, in the algebraic sense). I will focus mostly of networks of human face-to-face interactions and disease spreading over such, but also discuss the state of the field of temporal networks in general, and its future challenges.
[Download slides] (7,25 MB)
Petter Holme is a professor of Energy Science at Sungkyunkwan University, Korea. His research is both data driven and theoretical and covers many aspects of large-scale structures of natural and social systems. To do this, he mostly uses network-based frameworks. Lately he has been interested in how to integrate information about time with network methods. Holme has a PhD in theoretical physics from Umeå University, Sweden. After postdocs at the Department of Physics, University of Michigan, and Department of Computer Science, University of New Mexico, he was assistant professor at the School for Computer Science and Communication, Royal Institute of Technology, Sweden and Department of Physics, Umeå University, Sweden, before joining Sungkyunkwan University.
|Helen Margetts (Oxford Internet Institute, University of Oxford)|
The Computational Social Science of Turbulent Politics
Widespread use of social media is changing politics, by allowing 'tiny acts' of political participation which can accumulate in large-scale mobilizations through a series of chain reactions, where each act sends a signal to other actors, influencing their decision to join. The vast majority of these political mobilizations fail, but the ones that succeed are unpredictable, unstable and often unsustainable. This talk will discuss how we can research this new 'political turbulence' using two key computational social science methodologies. First, the modelling of large-scale transactional data can help to understand the distribution and shape of political mobilizations. Second, experiments can be used to test the effects of two forms of social influence - social information and visibility - that abound in social media environments and act as drivers to scale up collective action. In this way, politics is becoming simultaneously more unpredictable, through the leptokurtic distribution of mobilizations and the rapid rise of those that succeed - and more comprehensible, through the generation and analysis of large-scale data and experimental methods.
[Download slides] (3,10 MB)
Helen Margetts is the Director of the OII, and Professor of Society and the Internet. She is a political scientist specialising in digital era governance and politics, investigating political behaviour and political institutions in the age of the internet, social media and big data. She has published over a hundred books, articles and major research reports in this area, including Political Turbulence: How Social Media Shape Collective Action (with Peter John, scott Hale and Taha Yasseri, 2015); Paradoxes of Modernization (with Perri 6 and Christopher Hood, 2010); Digital Era Governance(with Patrick Dunleavy, 2006); and The Tools of Government in the Digital Age (with Christopher Hood, 2007). In 2003 she and Patrick Dunleavy won the ‘Political Scientists Making a Difference’ award from the UK Political Studies Association, in part for a series of policy reports onGovernment on the Internet for the UK National Audit Office (1999, 2002 and 2007), and she continues working to maximise the policy impact of her research. She is editor-in-chief of the journal Policy and Internet. She is a fellow of the Academy of Social Sciences and a Faculty Fellow of the Alan Turing Institute for Data Science.